1,345 research outputs found
A survey on 5G massive MIMO Localization
Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Waveform Design for 5G and Beyond
5G is envisioned to improve major key performance indicators (KPIs), such as
peak data rate, spectral efficiency, power consumption, complexity, connection
density, latency, and mobility. This chapter aims to provide a complete picture
of the ongoing 5G waveform discussions and overviews the major candidates. It
provides a brief description of the waveform and reveals the 5G use cases and
waveform design requirements. The chapter presents the main features of cyclic
prefix-orthogonal frequency-division multiplexing (CP-OFDM) that is deployed in
4G LTE systems. CP-OFDM is the baseline of the 5G waveform discussions since
the performance of a new waveform is usually compared with it. The chapter
examines the essential characteristics of the major waveform candidates along
with the related advantages and disadvantages. It summarizes and compares the
key features of different waveforms.Comment: 22 pages, 21 figures, 2 tables; accepted version (The URL for the
final version:
https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119333142.ch2
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Multi-service systems: an enabler of flexible 5G air-interface
Multi-service system is an enabler to flexibly support
diverse communication requirements for the next generation
wireless communications. In such a system, multiple types of
services co-exist in one baseband system with each service having
its optimal frame structure and low out of band emission (OoBE)
waveforms operating on the service frequency band to reduce the
inter-service-band-interference (ISvcBI). In this article, a
framework for multi-service system is established and the
challenges and possible solutions are studied. The multi-service
system implementation in both time and frequency domain is
discussed. Two representative subband filtered multicarrier
(SFMC) waveforms: filtered orthogonal frequency division
multiplexing (F-OFDM) and universal filtered multi-carrier
(UFMC) are considered in this article. Specifically, the design
methodology, criteria, orthogonality conditions and prospective
application scenarios in the context of 5G are discussed. We
consider both single-rate (SR) and multi-rate (MR) signal
processing methods. Compared with the SR system, the MR
system has significantly reduced computational complexity at the
expense of performance loss due to inter-subband-interference
(ISubBI) in MR systems. The ISvcBI and ISubBI in MR systems
are investigated with proposed low-complexity interference
cancelation algorithms to enable the multi-service operation in
low interference level conditions
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